Again, to decrease the regressor number, how about I collapse one first, then collapse the other? Suppose I have 120 regressors - they are from stimuli stage, and response stage. Thus I can first collapse the response stage regressors, so in the GLM (3dDeconolve), finally I have 60 onset regressors + 1 response regressors = 61 regressors. By this way, I can analyze the onset effect.

Then I collapse the onset regressors, but use the individual response stage regressors, so finally another 61 regressors. 1 onset regressor + 60 response regressors = 61 regressors. By this way, I can analyze the different response effect.

Is this way fine? I think it would be confusing if in a GLM (3dDeconolve), if there are too many regressors. But again, as I said, I really do not know how many is "too many", hence not good.